Automatic Goal Generation for Reinforcement Learning Agents (1705.06366v5)

Abstract: Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing. We use a generator network to propose tasks for the agent to try to achieve, specified as goal states. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment. Our method can also learn to achieve tasks with sparse rewards, which traditionally pose significant challenges.

Automatic Goal Generation for Reinforcement Learning Agents

The paper explores a novel approach to enhance the scope and efficiency of reinforcement learning (RL) agents by introducing a mechanism for automatic goal generation. Traditional RL methods focus on optimizing a single task defined by a specific reward function. However, the practical utility of RL can be significantly broadened if agents could autonomously discover and perform various tasks within their environment. This work presents a method to achieve this by integrating automatic goal generation into the RL paradigm.

Key Contributions

The proposed method leverages a generator network to create diverse tasks or goals that an agent can pursue. These goals are defined as reaching specific parameterized subsets of the state space. The generator network employs adversarial training techniques to ensure that the generated tasks are suitably challenging for the agent, effectively establishing an automatic curriculum.

The core contribution of this work is a Goal Generative Adversarial Network (Goal GAN), which dynamically adjusts to the agent's capabilities. The GAN framework includes a goal discriminator that evaluates the appropriateness of the task difficulty and a goal generator that formulates tasks matching this difficulty level. Such an adaptive curriculum facilitates the efficient learning of multiple tasks even in environments where reward signals are sparse.

Results and Implications

The approach demonstrates improved sample efficiency in learning to reach all feasible goals without prior knowledge of the environment. The experimental results underline the effectiveness of this method across various environments, showcasing a significant boost in learning speed over conventional techniques.

The implications of this research are substantial in multi-task learning contexts, like robotics, where agents need to operate across a range of objectives. The capability to autonomously generate appropriate tasks may reduce the need for extensive manual reward shaping and enable the deployment of RL systems in more dynamic and less predictable environments.

Future Perspectives

While the initial results are promising, future research could explore integrating this method with other multi-goal RL approaches, such as Hindsight Experience Replay (HER), to optimize goal selection. Furthermore, the development of hierarchical policies that leverage the learned goal-conditioned policies could open up new avenues for scaling RL to more complex decision-making tasks.

In summary, the introduction of automatic goal generation for RL agents positions this paper as a noteworthy advancement in the field. By autonomously expanding the agent's capacity to learn a wide array of tasks efficiently, this work lays the groundwork for more versatile and adaptable AI systems.